Tests for covariate‐associated heterogeneity in <i>IBD</i> allele sharing of affected relatives
Bibliographic record
Abstract
Linkage studies that aim to map susceptibility genes for complex diseases commonly test for excess allele sharing among affected relatives. Conventional methods based on identical-by-descent IBD allele sharing do not allow for possible differences among families, such as arise in the case of locus heterogeneity, and thus have reduced ability to detect linkage in the presence of such heterogeneity. We investigated two approaches to test for heterogeneity in allele sharing, using a family-level covariate that may be associated with different disease mechanisms leading to differences in allele sharing. Likelihood ratio tests for heterogeneity were formulated based on an extension of the linear and exponential likelihood models developed by Kong and Cox. Alternatively, we examined the asymptotic and permutation distributions of T-tests for differences between mean allele-sharing linkage scores from two covariate-defined family subgroups, assuming exchangeability. The size and power of heterogeneity tests were evaluated for S(all) and S(pairs) allele-sharing scoring functions using data sets of families with affected sibling and cousin pairs, generated under a model of locus heterogeneity. In certain simulation scenarios, the likelihood ratio test statistics did not follow the expected asymptotic distributions. The type I error estimates for the T-statistics conformed to nominal 5 and 1% levels in all scenarios considered, and corresponding power was comparable to that of the likelihood ratio tests. Application of these tests for heterogeneity detected significant differences in allele sharing between subgroups of families with inflammatory bowel disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".